The original input images are resized into five different scales {480,576,688,864,1200} concerning the smallest image dimension. The resized scale of a training image is randomly selected and the image is randomly horizontal flipped. In this way, each image is augmented into a total of ten images as many other WSOD methods do[1]}, [2]}, [3]}, [4]}. During network learning, we employ the SGD algorithm with momentum 0.9, weight decay \(5 \times 10^{-4}\)  and batch size 4. The model iterates 50K iterations, where the learning rate is set to \(5 \times 10^{-4}\)  for the first 20K iterations and then decreases to \(5 \times 10^{-5}\)  in the following 30K iterations. During testing, all ten augmented images are passed into the network, and take the averages as the final outputs. Non-maximum suppression is applied to all prediction with the IoU threshlod set to 0.3. Our experiments are implemented based on PyTorch deep learning framework and a NVIDIA GTX 1080Ti GPU.
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